Spotting the Difference: Context Retrieval and Analysis for Improved Forgery Detection and Localization
This addresses the need for more accurate and faster image forensics as tampering becomes more sophisticated, though it is incremental by building on existing retrieval ideas.
The paper tackles the problem of detecting and localizing image forgeries by using image search and retrieval to gather contextual clues, achieving improved performance on the Nimble dataset from NIST.
As image tampering becomes ever more sophisticated and commonplace, the need for image forensics algorithms that can accurately and quickly detect forgeries grows. In this paper, we revisit the ideas of image querying and retrieval to provide clues to better localize forgeries. We propose a method to perform large-scale image forensics on the order of one million images using the help of an image search algorithm and database to gather contextual clues as to where tampering may have taken place. In this vein, we introduce five new strongly invariant image comparison methods and test their effectiveness under heavy noise, rotation, and color space changes. Lastly, we show the effectiveness of these methods compared to passive image forensics using Nimble [https://www.nist.gov/itl/iad/mig/nimble-challenge], a new, state-of-the-art dataset from the National Institute of Standards and Technology (NIST).